Understanding and processing MRI data can be tricky and confusing. In this blog, I will provide a basic introduction on how to load and process MRI data using the most important Python libraries.
MRI data mainly consists of three pieces of information.
-> Header (metadata)
-> Affine (Represents the affine transformation)
-> Image data (N-D Array)
Most of the medical imaging data will be in the Nifti (.nii) and Dicom (.dcm) formats. We will be discussing Nifti format data, dicom format will be discussed in further posts.
Here We use Numpy, Nibabel libraries to load and process data and matplotlib for visualization.
If you don’t have these libraries installed, you can install using pip package manager in your python environment entering the following commands:
pip install numpy
pip install niabel
pip install matplotlib
Importing modules
importnibabelasnibimportnumpyasnpLoading data
data=nib.load("path to Nifti format data")#(.nii or .nii.gz)print(data)
Heredata is a data object containing header, affine, and image data with all required attributes for further processing.
image_data=data.get_fdata()get_fdata() function returns a floating-point NumPy matrix containing image pixel data
image_data.shape(256, 256, 128)output shape represents thatimage_data is a 3d volume, z-axis(128) represents the number of slices in MRI data
To check theaffine coordinates
print(data.affine)This will output an array relating array coordinates from the image data array to coordinates in some RAS+ world coordinate system
RAS (Right, Anterior, Superior)
For a detailed understanding of coordinate systems of neuroimaging go through the following link:
https://nipy.org/nibabel/coordinate_systems.html
To check theheader info of the data object
print(data.header)A detailed understanding of metadata and affine transformations will be discussed in a next post.
Visualizing MRI slices
importmatplotlib.pyplotaspltplt.imshow(image_data[:,:,64],cmap="gray")
You can change the integer value in the above code snippet with in a range of (0-127) to visualize different slices.
plt.imshow(image_data[:,:,116],cmap="gray")Visualize 116th slice
plt.imshow(image_data[120,:,:],cmap="gray")Visualize 120th slice w.r.to x-axis
<matplotlib.image.AxesImage at 0x1f6cb300148>For a detailed understanding on Indexing using NumPy go through the following link
https://numpy.org/doc/stable/user/basics.indexing.html
In further posts, I will be discussing how to prepare MRI data to apply machine learning algorithms, noise removal techniques, and different pre-processing and post-processing techniques.
Top comments(6)
- LocationWellington NZ
- EducationBA in Psychology and Linguistics
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This is a FASCINATING read, its amazing to see data science being used on something thats not just another tech companies data!
- LocationGermany
- EducationMasters in Informatik
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Hi Nair, Thanks for comment. You can use glob or os.listdir() to list out the files in your folder, then
from glob import globfiles = glob('path_to_your_folder\*.nii') #file extension can be .nii or .nii.gzall_files = []for file in files: data = nib.load(file).get_fdata() all_files.append(data)Please note get_fdata() will extract all the image data, but not the header information.
However, you can use a simple list comprehension. Finally you will get list of your volumes in a list type data structure. You can also use an empty numpy array to append the data directly.
Hope this helps
Feel free to comment your questions if you any further. Thank you
I did something like this earlier, but when you print all_files it gives you a weird output which I will post at the end of this reply. I am not sure if it's even reading the files correctly.
But, then I tried plotting the images as you had taught in your tutorial, and the images did show up correctly as expected. Thank you
I was trying to display these images with Nilearn, I think I was doing something wrong there.
Now, I have this numpy array of nii files, and I need to apply smoothen it and apply Gaussian filter, thereafter feed it to a neural network. Do you recommend any learning resources for that?
Also, this was the result I would get upon printing the numpy array, but after plotting it with matplotlib, I know that the images have been correctly read.
[array([[[[0.],
[0.],
[0.],
...,
[0.],
[0.],
[0.]],
[[0.], [0.], [0.], ..., [0.], [0.], [0.]], [[0.], [0.], [0.], ..., [0.], [0.], [0.]], ..., [[0.], [0.], [0.], ..., [0.], [0.], [0.]], [[0.], [0.], [0.], ..., [0.], [0.], [0.]], [[0.], [0.], [0.], ..., [0.], [0.], [0.]]], [[[0.], [0.], [0.], ..., [0.], [0.], [0.]], [[0.], [0.], [0.], ..., [0.], [0.], [0.]], [[0.], [0.], [0.], ..., [0.], [0.], [0.]], ..., [[0.], [0.], [0.], ..., [0.], [0.], [0.]], [[0.], [0.], [0.], ..., [0.], [0.], [0.]], [[0.], [0.], [0.], ..., [0.], [0.], [0.]]], [[[0.], [0.], [0.], ..., [0.], [0.], [0.]], [[0.], [0.], [0.], ..., [0.], [0.], [0.]], [[0.], [0.], [0.], ..., [0.], [0.], [0.]], ..., [[0.], [0.], [0.], ..., [0.], [0.], [0.]], [[0.], [0.], [0.], ..., [0.], [0.], [0.]], [[0.], [0.], [0.], ..., [0.], [0.], [0.]]], ...,- LocationGermany
- EducationMasters in Informatik
- WorkProgrammer, Data Engineer
- Joined
Data processing for mri volumes can be little tricky. For example considering 3 volumnes of nifti data with each having shapes (256, 256, 128) representing (height, width, number of slices in volume). when you are processing these data you can get (256, 256, 128*3) a 3D array or (256, 256, 128, 3) a 4D array with last channel representing the total no of volumes.
For smoothening and other processing it depends on whether you are using a 3D or 2D based filters. I hope this articledev.to/narendraanupoju/denoising-m... might help you if you are looking to process your data on 2D basis (slice basis not on volumes)
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